Conclusion
Key Takeaways
- Quantitative research uses numerical data and statistical analysis to study relationships and cause-and-effect associations between variables
- The quantitative research process is objective, systematic, and rigorous, reducing subjective bias
- Quantitative research produces reliable and repeatable information. Findings are generalizable to larger populations, and results are anonymous.
- Quantitative research can be expensive and resource-heavy. It provides limited depth, requires a larger sample size, and may create an unnatural environment.
- Observational studies involve no intervention; the researchers simply observe and measure variables
- Experimental studies involve researchers assigning an intervention to study cause-and-effect relationships
- Observational studies include descriptive and analytical studies
- Descriptive studies aim to describe characteristics of a population or phenomenon
- Types of descriptive studies include: case reports/case series, ecological studies, and cross-sectional studies
- Analytical studies aim to identify associations or causes
- Types of analytical studies include case-control studies and cohort studies
- Experimental study designs include: randomized controlled trials and non-randomized controlled trials
- Systematic reviews, meta-analyses, and randomized controlled trials provide the highest level of evidence
- Experiments are designed to test causal relationships between an independent variable and a dependent variable
- True experiments require active manipulation of the independent variable by the researcher
- Extraneous variables are factors other than the independent/dependent variable that can affect results. If extraneous variables vary systematically with the independent variable, they become confounding variables, therefore threatening validity
- Non-experimental research involves measuring variables as they naturally occur, without manipulation of an independent variable
- Experimental research is preferred when the goal is to explain
- Non-experimental research is better suited for describing or predicting
- Types of non-experimental research include correlational and observational research
- Quasi-experimental research involves an intervention or treatment, but lacks random assignment of participants to groups
- Quasi-experimental research allows researchers to study interventions in naturalistic, practical environments; more ethical and feasible than true experiments in many applied fields
- Primary data is collected directly for the study, such as surveys, questionnaires, observations, and clinical measurements
- Secondary data is from pre-existing sources such as hospital records, national surveys
- Probability sampling ensures generalizability. Methods include: simple random sampling, stratified sampling, systematic sampling, cluster sampling.
- Non-probability sampling is more common in qualitative studies. Methods include: convenience sampling and purposive sampling.
- Statistics involves collecting, processing, analyzing, presenting and interpreting data
- Types of variables include numerical variables and categorical variables. Numerical variables include discrete (whole numbers) and continuous (fractions/decimals). Categorical variables include nominal (no order) and ordinal (ranked order).
- Descriptive statistics explain how different variables in a sample or population relate to one another. Includes central tendency measurements like mean (average), median (middle), and mode (most frequent) and measures of dispersion such as range, variance, and standard deviation (spread of data)
- Inferential statistics draw conclusions or inferences about a whole population from a random sample of data. You must first define your research question and state the null and alternative hypotheses, then choose the test based on variable type and distribution, set the significance level, run the test, and interpret the p-value. If p < 0.05 you reject the null hypothesis; if p > 0.05 you retain the null hypothesis
- Correlation shows strength and direction of a relationship
- Regression predicts outcomes and examines the influence of independent variables on dependent variables
Knowledge Check